Ryotaro Shimizu 교수

In recent years, explainable recommendation has been a topic of active study. This is because the branch of the machine learning field related to methodologies is enabling human understanding of the reasons for the outputs of recommender systems. The realization of explainable recommendation is widely expected to increase both user satisfaction and the demand for explainable recommendation systems. Explainable recommendation utilizes a wealth of side information (such as sellers, brands, user ag

In recent years, it has become common for consumers to familiarize themselves with the latest fashion trends through the internet and engage in their own fashion-inspired shopping activities. Therefore, making fashion-inspired shopping and browsing activities (internet surfing in the fashion domain) comfortable is essential because it leads to interactions in the fashion industry. However, fashion is a fuzzy and complex domain that contains many abstract elements, and this ambiguity and complexi

A novel technology named fashion intelligence system has been proposed to quantify ambiguous expressions unique to fashion, such as "casual," "adult-casual," and "office-casual," and to support users’ understanding of fashion. However, the existing visual-semantic embedding (VSE) model, which is the basis of its system, does not support situations in which images are composed of multiple parts such as hair, tops, pants, skirts, and shoes. We propose partial VSE, which enables sensitive learning

Recently, credit cards with point rewards functions (rewards credit cards) are widely used. Credit card companies can collect the users’ usage log data of various stores in multiple industries. The purposes of possessing a credit card varies depending on each user such as to use only the credit function, to use both the credit and point rewards functions, etc. Moreover, credit cards can be used in various situations in users’ lives, and the purchase history of each user is di

Recent research on explainable recommendation generally frames the task as a standard text generation problem, and evaluates models simply based on the textual similarity between the predicted and ground-truth explanations. However, this approach fails to consider one crucial aspect of the systems: whether their outputs accurately reflect the users' (post-purchase) sentiments, i.e., whether and why they would like and/or dislike the recommended items. To shed light on this issue, we introduce ne

Due to the recent development of electronic commerce (EC) technology, sale amounts on EC sites have increased rapidly. Large amounts of consumer purchase history data can now easily be obtained, and many data-based approaches to identifying consumer purchase trends have been studied. On the other hand, since diversification of consumer consciousness such as their values and lifestyles are important for marketing research, many approaches to identifying the relationship between “consumers' values

• Domain adaptation information gain-based target-related data selection is proposed. • Proposed DAIG improves target model accuracy via target-related data selection. • DAIG effectively gathers relevant data from source to benefit target tasks. • DAIG extracts “rough prior” from target data for source data pre-training. • DAIG-guided selection outperforms baselines in multiple experimental settings. In recent years, due to the explosive popularity of large-scale pre-trained models such as large

In 2015, Nepalese people faced two serious problems: a number of massive earthquakes, and a political crisis. After the end of the civil war in 2006, the Nepalese government worked for several years on drafting the new constitution; however, it could not be realized due to political disagreements. Triggered by a massive earthquake in April 2015, the general opinion was to establish the constitution, and it was finally ratified in September, 2015. However, it was not accepted by the Madhesi, one